Bioremediation of Zinc Using Bacillus sp. Isolated from Metal- Contaminated Industrial Zone

The production of heavy metals has increased quickly since the industrial revolution. Heavy metals frequently form compounds that can be toxic, carcinogenic, or mutagenic, even in very small concentrations. The usual techniques of removing metals from wastewaters are in general expensive and have many restrictions. Alternative methods of metal removal and recovery based on biological materials have been measured. Among various agents, the use of microbes for the removal of metals from industrial and municipal wastewater has been proposed as a promising alternative to conventional heavy metal management strategies in past decades. Thus, the present study aims to isolate and characterize bacteria from soil, sediment, and waters of metal-contaminated industrial area to study the zinc resistance patterns and the zinc bioaccumulation potential of the selected microorganism. Zinc analysis of the samples revealed that concentrations varying from 39.832 m g/L to 310.24 m g/L in water, 12.81 m g/g to 407.53 m g/g in soil, and 81.06 m g/g to 829.54 m g/g in sediment are present. Bacterial zinc resistance study showed that tolerance to Zn was relatively low (<500 m g/ml). Ten bacterial genera were represented in soil and 11 from water, while only 5 bacterial genera were recorded from sediment samples. Bacillus, Pseudomonas , and Enterobacter were found in soil, sediment, and water samples. Highly zincresistant Bacillus sp. was selected for zinc removal experiment. Zinc removal studies revealed that at pH 5 about 40% reduction occurs; at pH 7, 25% occurs; and at pH 9, 50% occurs. Relatively an increased removal of Zinc was observed in the fi rst day of the experiment by Bacillus sp. The metal bioaccumulative potential of the selected isolates may have possible applications in the removal and recovery of zinc from industrial ef fluents.

Coastal regulation zone rules in coastal panchayats (villages) of Kerala, India vis-a` -vis socio-economic impacts from the recently introduced peoples’ participatory program for local self-governance and sustainable development

Coastal Regulation Zone (CRZ) notification was issued by the Ministry of Environment and Forest of Government of India in February 1991 as a part of the Environmental Protection Act of 1986 to protect the coast from eroding and to preserve its natural resources. The initial notification did not distinguish the variability and diversity of various coastal states before enforcing it on the various states and Union Territories. Impact assessments were not carried out to assess its impact on socio-economic life of the coastal population. For the very same reason, it was unnoticed or rather ignored till 1994 when the Supreme Court of India made a land mark judgment on the fate of the coastal aquaculture which by then had established as an economically successful industry in many South Indian States. Coastal aquaculture in its modern form was a prohibited activity within CRZ. Lately, only various stakeholders of the coast realized the real impact of the CRZ rules on their property rights andbusiness. To overcome the initial drawbacks several amendments were made in the regulation to suit regional needs. In 1995, another great transformation took place in the State of Kerala as a part of the reorganization of the local self government institutions into a decentralized three tier system called ‘‘Panchayathi Raj System’’. In 1997, the state government also decided to transfer the power with the required budget outlay to the grass root level panchayats (villages) and municipalities to plan and implement the various projects in their localities with the full participation of the local people by constituting Grama Sabhas (Peoples’ Forum). It is called the ‘‘Peoples’ Planning Campaign’’(Peoples’ Participatory Programme—PPP for Local Level Self-Governance). The management of all the resources including the local natural resources was largely decentralized to the level of local communities and villages. Integrated, sustainable coastal zone management has become the concern of the local population. The paper assesses the socio-economic impact of the centrally enforced CRZ and the state sponsored PPP on the coastal community in Kerala and suggests measures to improve the system and living standards of the coastal people within the framework of CRZ.

Predicted impact of the sea-level rise at VellareColeroon estuarine region of Tamil Nadu coast in India: Mainstreaming adaptation as a coastal zone management option

Low-lying coastal areas are more vulnerable to the impacts of climate change as they are highly prone for inundation to SLR (Sea-Level Rise). This study presents an appraisal of the impacts of SLR on the coastal natural resources and its dependent social communities in the low-lying area of VellareColeroon estuarine region of the Tamil Nadu coast, India. Digital Elevation Model (DEM) derived from SRTM 90M (Shuttle Radar Topographic Mission) data, along with GIS (Geographic Information System) techniques are used to identify an area of inundation in the study site. The vulnerability of coastal areas in Vellar-Coleroon estuarine region of Tamil Nadu coast to inundation was calculated based on the projected SLR scenarios of 0.5 m and 1 m. The results demonstrated that about 1570 ha of the LULC (Land use and Land cover) of the study area would be permanently inundated to 0.5 m and 2407 ha for 1 m SLR and has also resulted in the loss of three major coastal natural resources like coastal agriculture, mangroves and aquaculture. It has been identified that six hamlets of the social communities who depend on these resources are at high-risk and vulnerable to 0.5 m SLR and 12 hamlets for 1 m SLR. From the study, it has been emphasized that mainstreaming adaptation options to SLR should be embedded within a coastal zone management and planning effort, which includes all coastal natural resources (ecosystem-based adaptation), and its dependent social communities (community-based adaptation) involved through capacity building

Standard of Living and Community Perception in the Community Based Ecotourism (CBET) Sites of Kerala: An Inter Zone Analysis

This study is an attempt to situate the quality of life and standard of living of local communities in ecotourism destinations inter alia their perception on forest conservation and the satisfaction level of the local community. 650 EDC/VSS members from Kerala demarcated into three zones constitute the data source. Four variables have been considered for evaluating the quality of life of the stakeholders of ecotourism sites, which is then funneled to the income-education spectrum for hypothesizing into the SLI framework. Zone-wise analysis of the community members working in tourism sector shows that the community members have benefited totally from tourism development in the region as they have got both employments as well as secured livelihood options. Most of the quality of life-indicators of the community in the eco-tourist centres show a promising position. The community perception does not show any negative impact on environment as well as on their local culture.

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.

Decomposition of and nutrient release from ruminant manure on acid sandy soils in the Sahelian zone of Niger, West Africa

In ago-pastoral systems of the semi-arid West African Sahel, targeted applications of ruminant manure to the cropland is a widespread practice to maintain soil productivity. However, studies exploring the decomposition and mineralisation processes of manure under farmers' conditions are scarce. The present research in south-west Niger was undertaken to examine the role of micro-organisms and meso-fauna on in situ release rates of nitrogen (N), phosphorus (P) and potassium (K) from cattle and sheep-goat manure collected from village corrals during the rainy season. The results show tha (1) macro-organisms played a dominant role in the initial phase of manure decomposition; (2) manure decomposition was faster on crusted than on sandy soils; (3) throughout the study N and P release rates closely followed the dry matter decomposition; (4) during the first 6 weeks after application the K concentration in the manure declined much faster than N or P. At the applied dry matter rate of 18.8 Mg ha^-1, the quantities of N, P and K released from the manure during the rainy season were up to 10-fold larger than the annual nutrient uptake of pearl millet (Pennisetum glaucum L.), the dominant crop in the traditional agro-pastoral systems. The results indicate considerable nutrient losses with the scarce but heavy rainfalls which could be alleviated by smaller rates of manure application. Those, however, would require a more labour intensive system of corralling or manure distribution.

Causes of legume-rotation effects in increasing cereal yields across the Sudanian, Sahelian and Guinean zone of West Africa

On-farm experiments and pot trials were conducted on eight West African soils to explore the mechanisms governing the often reported legume rotation-induced cereal growth increases in this region. Crops comprised pearl millet (Pennisetum glaucum L.), sorghum (Sorghum bicolor Moench), maize (Zea mays L.), cowpea (Vigna unguiculata Walp.) and groundnut (Arachis hypogaea L.). In groundnut trials the observed 26 to 85% increases in total dry matter (TDM) of rotation cereals (RC) compared with continuous cereals (CC) in the 4th year appeared to be triggered by site- and crop-specific early season differences in nematode infestation (up to 6-fold lower in RC than in CC), enhanced Nmin and a 7% increase in mycorrhizal (AM) infection. In cowpea trials yield effects on millet and differences in nematode numbers, Nmin and AM were much smaller. Rhizosphere studies indicated effects on pH and acid phosphatase activity as secondary causes for the observed growth differences between RC and CC. In the study region legume-rotation effects on cereals seemed to depend on the capability of the legume to suppress nematodes and to enhance early N and P availability for the subsequent cereal.

Evaluating partial root-zone irrigation and mulching in okra (Abelmoschus esculentus L.) under a sub-humid tropical climate

The field experiments were conducted to compare the alternate partial root-zone irrigation (APRI) with and without black plastic mulch (BPM) with full root-zone irrigation (FRI) in furrow-irrigated okra (Abelmoschus esculentus L. Moench) at Bhubaneswar, India. APRI means that one of the two neighbouring furrows was alternately irrigated during consecutive watering. FRI was the conventional method where every furrow was irrigated during each watering. The used irrigation levels were 25% available soil moisture depletion (ASMD), 50% ASMD, and 75% ASMD. The plant growth and yield parameters were observed to be significantly (p < 0.05) higher with frequent irrigation (at 25% ASMD) under all irrigation strategies. However, APRI + BPM produced the maximum plant growth and yield using 22% and 56% less water over APRI without BPM and FRI, respectively. The highest pod yield (10025 kg ha^-1) was produced under APRI at 25% ASMD + BPM, which was statistically at par with the pod yield under APRI at 50% ASMD + BPM. Irrigation water use efficiency (IWUE), which indicates the pod yield per unit quantity of irrigation water, was estimated to be highest (12.3 kg m^-3) under APRI at 50% ASMD + BPM, followed by APRI at 25% ASMD + BPM. Moreover, the treatment APRI at 50% ASMD + BPM was found economically superior to other treatments, generating more net return (US $ 952 ha^-1) with higher benefit–cost ratio (1.70).

Dairy farms typology and management of animal genetic resources in the peri-urban zone of Bamako (Mali)

Facing growth in demand, dairy production in peri-urban areas of developing countries is changing rapidly. To characterise this development around Bamako (Mali), this study establishes a typology of dairy production systems with a special focus on animal genetic resources. The survey included 52 dairy cattle farms from six peri-urban sites. It was conducted in 2011 through two visits, in the dry and harvest seasons. The median cattle number per farm was 17 (range 5-118) and 42% of farmers owned cropland (8.3 +/- 7.3 ha, minimum 1 ha, maximum 25 ha). Feeding strategy was a crucial variable in farm characterisation, accounting for about 85% of total expenses. The use of artificial insemination and a regular veterinary follow-up were other important parameters. According to breeders’ answers, thirty genetic profiles were identified, from local purebreds to different levels of crossbreds. Purebred animals raised were Fulani Zebu (45.8%), Maure Zebu (9.2%), Holstein (3.0%), Azawak Zebu (1.3%), Mere Zebu (0.5%) and Kuri taurine (0.1%). Holstein crossbred represented 30.5% of the total number of animals (19.0% Fulani-Holstein, 11.2% Maure-Holstein and 0.3% Kuri-Holstein). Montbéliarde, Normande and Limousin crossbreds were also found (6.6%, 0.7% and 0.3%, respectively). A multivariate analysis helped disaggregate the diversity of management practices. The high diversity of situations shows the need for consideration of typological characteristics for an appropriate intervention. Although strongly anchored on local breeds, the peri-urban dairy systems included a diversity of exotic cattle, showing an uncoordinated quest of breeders for innovation. Without a public intervention, this dynamic will result in an irremediable erosion of indigenous animal genetic resources.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.

Keyword Join: Realizing Keyword Search in P2P-based Database Systems

In this paper, we present a P2P-based database sharing system that provides information sharing capabilities through keyword-based search techniques. Our system requires neither a global schema nor schema mappings between different databases, and our keyword-based search algorithms are robust in the presence of frequent changes in the content and membership of peers. To facilitate data integration, we introduce keyword join operator to combine partial answers containing different keywords into complete answers. We also present an efficient algorithm that optimize the keyword join operations for partial answer integration. Our experimental study on both real and synthetic datasets demonstrates the effectiveness of our algorithms, and the efficiency of the proposed query processing strategies.

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task